code: def test_transform_meta_override():
      X = pd.DataFrame({"cat_s": ["a", "b", "c", "d"]})
      dd_X = dd.from_pandas(X, npartitions=2)
  
      base = OneHotEncoder(sparse_output=False)
      base.fit(pd.DataFrame(X))
  
      # Failure when not proving transform_meta
      # because of value dependent model
      wrap = ParallelPostFit(base)
      with pytest.raises(ValueError):
          wrap.transform(dd_X)
  
      wrap = ParallelPostFit(
          base,
          transform_meta=np.array([[0, 0, 0, 0]], dtype=np.float64),
      )
      result = wrap.transform(dd_X)
      expected = base.transform(X)
      assert_eq_ar(result, expected)
  
imports: import dask
  import dask.array
  import dask.dataframe
  import numpy
  import pandas
  import pytest
  import sklearn.datasets
  from scipy.sparse import csr_matrix
  from sklearn.decomposition import PCA
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier
  from sklearn.naive_bayes import CategoricalNB
  from sklearn.preprocessing import OneHotEncoder
  from dask_ml.datasets import make_classification
  from dask_ml.utils import assert_eq_ar, assert_estimator_equal
  from dask_ml.wrappers import ParallelPostFit
label_desc: Use image node_id 6 for calling a global function with example usage: test_transform_meta_override() without return types